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<p>Renewable Energy Systems and Electrical Vehicles are penetrating the power system exponentially. This penetration of both systems reckons stable working of the overall system and power balancing in the system to maintain frequency and voltage stability. This paper employs soft computing techniques to achieve power balancing in a photovoltaic (PV)-integrated grid with electric vehicle (EV) loads. Power system stability is improved through the use of an orthogonal real and reactive power controller. Soft computing techniques like Fuzzy Logic Controller and Adaptive Neuro-Fuzzy Inference System(ANFIS) are applied to the Orthogonal Controller (DQ controller) to control the power quality disturbances. The system consists of two loops: the power stability loop, which regulates the Voltage Source Inverter (VSI) connecting the photovoltaic (PV) system to the grid, and the bidirectional power flow loop, designed to manage vehicle-to-grid and grid-to-vehicle power exchanges. A MATLAB based simulation is developed using Fuzzy Logic Controller (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers to visualize their performance. The performance enhancement of these soft computing techniques is evaluated in comparison to the traditional Proportional-Integral (PI) controller. The topology of the bidirectional controller is adopted with dual converters instead of a single bidirectional converter in the proposed study. Although the cost of the setup increases, but the adoption in performance-intensive applications would make it a relevant topological variation. It is observed that the advanced soft computing techniques of FLC and ANFIS performed better than the PI controller considering harmonic reduction, power factor enhancement and voltage regulation as the criterion of performance. Considering advanced algorithms for the performance evaluation has given a good percentage change in harmonic reduction which is measured in terms of Total Harmonic Distortion. A significant improvement in the THD is observed while using the ANFIS algorithm as opposed while using the PI controller. The results demonstrated by the ANFIS controller surpassed the other controllers in minimizing THD and effectively managing the combined impact of PV penetration and EV loading on the grid.</p>

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Power quality control in electric vehicle connected PV-Grid using soft computing techniques

  • 期刊论文

  • 发表日期：

    2025年7月1日

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  • Kishore MP,
  • Ashish Shrivastava,
  • Amit Soni

摘要

Renewable Energy Systems and Electrical Vehicles are penetrating the power system exponentially. This penetration of both systems reckons stable working of the overall system and power balancing in the system to maintain frequency and voltage stability. This paper employs soft computing techniques to achieve power balancing in a photovoltaic (PV)-integrated grid with electric vehicle (EV) loads. Power system stability is improved through the use of an orthogonal real and reactive power controller. Soft computing techniques like Fuzzy Logic Controller and Adaptive Neuro-Fuzzy Inference System(ANFIS) are applied to the Orthogonal Controller (DQ controller) to control the power quality disturbances. The system consists of two loops: the power stability loop, which regulates the Voltage Source Inverter (VSI) connecting the photovoltaic (PV) system to the grid, and the bidirectional power flow loop, designed to manage vehicle-to-grid and grid-to-vehicle power exchanges. A MATLAB based simulation is developed using Fuzzy Logic Controller (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers to visualize their performance. The performance enhancement of these soft computing techniques is evaluated in comparison to the traditional Proportional-Integral (PI) controller. The topology of the bidirectional controller is adopted with dual converters instead of a single bidirectional converter in the proposed study. Although the cost of the setup increases, but the adoption in performance-intensive applications would make it a relevant topological variation. It is observed that the advanced soft computing techniques of FLC and ANFIS performed better than the PI controller considering harmonic reduction, power factor enhancement and voltage regulation as the criterion of performance. Considering advanced algorithms for the performance evaluation has given a good percentage change in harmonic reduction which is measured in terms of Total Harmonic Distortion. A significant improvement in the THD is observed while using the ANFIS algorithm as opposed while using the PI controller. The results demonstrated by the ANFIS controller surpassed the other controllers in minimizing THD and effectively managing the combined impact of PV penetration and EV loading on the grid.